More severe pneumonitis in children predicts the need for admission and elevation of some but not all markers of severe Covid-19.

Unlike most other viral pneumonitis, SARS-CoV-2 often causes hyperferritinemia, elevations in D-dimer, lactate dehydrogenase (LDH), transaminases, troponin, CRP, and other inflammatory markers. We questioned (1) if the severity of pneumonitis observed on lung ultrasound was associated with hospitalization and (2) could lung ultrasound be used to stratify which children needed blood tests? MethodsWe did a retrospective cross-sectional review of children aged between 14 days and 21 years of age being evaluated for Covid-19 in our pediatric emergency department from 30/November/2019 to 14/August/2021 who had had a point-of-care lung ultrasound. Lung ultrasounds were categorized using a 6-point ordinal scale. We used logistic regression to estimate the adjusted effect of lung ultrasound on hospital admission. We performed ordinary least square regression for the association between lung ultrasound severity and laboratory abnormalities. We adjusted these using propensity score derived inverse probability weighting to account for the non-random decision to obtain laboratory investigations. ResultsWe identified 500 point-of-care lung ultrasounds of which 427 could be assigned a severity category. Increasing lung ultrasound severity was associated with increased hospital admission OR 1.36(95% CI 1.08, 1.72.) Ferritin, LDH, transaminases, and D-dimer, but not CRP or troponin were significantly associated with more than moderately severe lung ultrasounds. D-Dimer, CRP, and troponin were sometimes elevated even when lung ultrasound was normal. ConclusionSeverity of pneumonitis was associated with hospital admission. Ferritin, LDH, transaminases, and D-dimer were increased in more than moderately severe pneumonitis but lung ultrasound did not predict elevation of other markers.

Performance of Diabetes and Kidney Disease Screening Scores in Contemporary United States and Korean Populations

Background@#Risk assessment tools have been actively studied, and they summarize key predictors with relative weights/importance for a disease. Currently, standardized screening scores for type 2 diabetes mellitus (DM) and chronic kidney disease (CKD)—two key global health problems—are available in United States and Korea. We aimed to compare and evaluate screening scores for DM (or combined with prediabetes) and CKD, and assess the risk in contemporary United States and Korean populations. @*Methods@#Four (2×2) models were evaluated in the United States-National Health and Nutrition Examination Survey (NHANES 2015–2018) and Korea-NHANES (2016–2018)—8,928 and 16,209 adults. Weighted statistics were used to describe population characteristics. We used logistic regression for predictors in the models to assess associations with study outcomes (undiagnosed DM and CKD) and diagnostic measures for temporal and cross-validation. @*Results@#Korean adult population (mean age 47.5 years) appeared to be healthier than United States counterpart, in terms of DM and CKD risks and associated factors, with exceptions of undiagnosed DM, prediabetes and prehypertension. Models performed well in own country and external populations regarding predictor-outcome association and discrimination. Risk tests (high vs. low) showed area under the curve >0.75, sensitivity >84%, specificity >45%, positive predictive value >8%, and negative predictive value >99%. Discrimination was better for DM, compared to the combined outcome of DM and prediabetes, and excellent for CKD due to age. @*Conclusion@#Four easy-to-use screening scores for DM and CKD are well-validated in contemporary United States and Korean populations. Prevention of DM and CKD may serve as first-step in public health, with these self-assessment tools as basic tools to help health education and disparity.

The Impact of Legal Procedures on Hospital Length of Stay: Balancing Legal and Clinical Concerns / 정신간호학회지

Purpose@#Psychiatric hospital length of stay (LOS) is not affected solely by socio-clinical factors but also by legal procedures. This study examined the associations between legal procedures and LOS. @*Methods@#Data from 521 patients with psychiatric illnesses hospitalized over 2013-2015 were analyzed. Logistic regression was used to evaluate the predictors of longer (> 14 days) or prolonged (> 30) LOS with socio-clinical factors and legal procedures including court-ordered interventions (assisted outpatient treatment, medication over objection, and retention). @*Results@#Longer LOS occurred in 246 patients and 99 had prolonged LOS. Legal procedures affected 57 patients, with 11 assisted outpatient treatments, 39 cases of medication over objection, and 16 retentions. Longer LOS was significantly associated with six factors including older age, unmarried status, non-Hispanic race, risk of violence, schizophrenia, and legal procedures. Legal procedures had the strongest association. Longer/prolonged LOS yielded qualitatively similar associations. @*Conclusion@#Among 521 psychiatric inpatients, approximately 11% were mandated to receive interventions/procedures by the courts. Court-ordered legal procedures were strongly associated with longer LOS. Mental health providers may consider legal procedures for patients at high treatment/ medication noncompliance risk as early as patient admission to inpatient units to prevent, intervene or prepare for a longer or prolonged LOS.

Point of care lung ultrasound is useful when screening for CoVid-19 in Emergency Department patients.

BackgroundCoVid-19 can be a life-threatening lung disease or a trivial upper respiratory infection depending on whether the alveoli are involved. Emergency department (ED) screening in symptomatic patients with normal vital signs is frequently limited to oro-nasopharyngeal swabs. We tested the null hypothesis that patients being screened for CoVid-19 in the ED with normal vital signs and without hypoxia would have a point-of-care lung ultrasound (LUS) consistent with CoVid-19 less than 2% of the time. MethodsO_ST_ABSSubjectsC_ST_ABSSubjects were identified from ED ultrasound logs. Inclusion criteriaAge 14 years or older with symptoms prompting ED screening for CoVid-19. Exclusion criteriaKnown congestive heart failure or other chronic lung condition likely to cause excessive B lines on LUS. InterventionStructured blinded ultrasound review and chart review AnalysisWe used a two-sided exact hypothesis test for binomial random variables. We also measured LUS diagnostic performance using computed tomography as the gold standard. ResultsWe reviewed 77 charts; 49 met inclusion criteria. Vital signs were normal in 30/49 patients; 10 (33%) of these patients had LUS consistent with CoVid-19. We rejected the null hypothesis (p-value < 0.001). The treating physicians interpretation of their own point of care lung ultrasounds had a sensitivity of 100% (95% CI 75%, 100%) and specificity of 80% (95% CI 68%, 89%). ConclusionLUS has a meaningful detection rate for CoVid-19 in symptomatic ED patients with normal vital signs. We recommend at least LUS be used in addition to PCR testing when screening symptomatic ED patients for CoVid-19. Capsule What is knownAuscultation and chest x-ray are insufficient to screen for lung involvement when SARS-CoV-2 infection is suspected. Point of care lung ultrasound is widely available, safer, and less resource intensive than CT imaging. What we foundIn symptomatic patients presenting to the ED even those with normal vital signs had point of care lung ultrasound evidence of alveolar level involvement 33%of patients. Point of care lung ultrasound was 100% sensitive and 80% specific compared to CT (reference standard) when evaluating patients for Covid-19. What this addsPoint of care lung ultrasound or similar imaging should performed when screening symptomatic patients in whom SARS-CoV-2 infection is suspected.

Comparison of Endoscopic Ultrasound Biopsy Needles for Endoscopic Ultrasound-Guided Liver Biopsy

BACKGROUND/AIMS: To compare the performance of latest commercially available endoscopic ultrasound biopsy needles. METHODS: Six latest commercially available needles were tested on a freshly harvested bovine liver; the tested needles included three 19 G, one 20 G, and two 22 G needles. Five biopsies were performed per needle with 10 mL of wet suction. The primary outcome was the number of complete portal tracts (CPTs) per needle aspirate. The secondary outcomes were the mean specimen length and mean fragment length. Analysis of variance and Tukey’s test were applied. RESULTS: All 19 G needles and the 20 G needle yielded similar mean CPTs and were superior to the SharkCore 22 G needle (p<0.001 adjusted for multiplicity). There was no statistically significant difference in total specimen length among the three 19 G needles and the 20 G needle tested. The two 22 G needles performed similarly with respect to the number of CPTs, mean fragment length, and mean specimen length (adjusted p=0.07, p=0.59, and p=0.10, respectively). CONCLUSIONS: The specimen adequacy was similar among the 3 latest commercially available 19 G needles. The endoscopist may choose a larger-bore needle based on availability without concerns of specimen adequacy. Further studies are needed to assess the ease of needle use in various anatomical locations and to confirm the optimal needle design.

How to Establish Clinical Prediction Models

A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.